Project Summary We aim to develop novel computational approaches to improve detection of risk genes and prediction of functional effects of germline mutations in patients with developmental disorders by integrating somatic cancer mutation and functional genomic data. Developmental disorders (DD), including neurodevelopmental disorders (NDD) and structural birth defects, affect ~5% of all newborns and have a significant impact on families and society. In the past few years, large-scale family-based sequencing studies on DD, such as autism and congenital heart disease, have identified a large number of de novo variants potentially implicated in disease. Unlike many other pediatric Mendelian diseases, genetic diagnosis of DD by genome or exome sequencing is more challenging because: (a) the complete catalog of DD genes (likely ~1,000) is not yet available; (b) observed variants are often difficult to interpret due to lack of rapid and cost-effective functional assays. Therefore, improved ability to identify novel risk genes and predict the functional effects of missense variants would significantly improve our ability to diagnose DD and develop targeted therapeutic approaches. Cancer is driven by dysregulation of core cellular processes that are also important to DD, such as proliferation, growth, and differentiation. There are well known genes implicated in both cancer and DD with somatic driver mutations in cancer and highly- penetrant germline de novo variants in DD. We analyzed data from recent large-scale genomic studies of cancer and DD, and found a large number of genes potentially implicated in both diseases, and many of them have similar molecular modes of action across conditions. This indicates that patterns of cancer somatic mutations can provide valuable insights to improve our ability to identify causal variants and genes in patients with DD. To that end, we have these specific aims: Specific Aim 1. Elucidate common genes and variants disrupted in cancer and DD based on somatic mutations in cancer and germline de novo mutations in DD. Specific Aim 2. Infer dosage sensitive genes by integrating mutation data in cancer and developmental disorders with functional genomic data. Specific Aim 3. Software development and data sharing. With the proposed new computational approaches, we will be able to leverage the accumulating cancer somatic mutation data from international cancer precision medicine efforts. In this framework, tumor samples will be natural ?laboratories? for large-scale functional assays in cancer driver genes. This strategy will improve the utility of cross-field genomic data, and allow us to better predict functional effects of candidate variants (especially missense variants) in genetic diagnosis and identify novel risk genes for developmental disorders.